“Statistics” is one of the basic subjects in the second course of the Industrial Technology Engineering Degree, and it is placed in the Basic Formation module. The work developed in this subject will allow the students solve basic probability problems as well as develop tools and statistic methods such as sampling, estimation and hypothesis contrasting.

For an optimal development of the subject “statistics”, knowledge on equation solving and operational skills on mathematic expressions are necessary.

Module M01CM01 competence:



Capacity to solve engineering mathematical problems.



Ability to apply knowledge on: lineal algebra; geometry; differential geometry; differential calculus and integration; differential equations and partial derivatives; numeric methods; numeric algorithms; statistics and optimization.



Learning results:



Clear and organized exposition of basic statistics concepts.



Resolution of specific problems, using in an appropriate way the multiple representations of a phenomenon. All the steps for the problem resolution must be clear and accurate.



Elaboration and oral presentation of works regarding the knowledge acquired in the module and applied to general and practical problems regarding industrial engineering.

LESSON 1: PROBABILITY AXIOMATICS

LESSON 2: CALCULATING PROBABILITY

LESSON 3: ONE DIMENSION RANDOM VARIABLE

LESSON 4: TWO DIMENSIONS RANDOM VARIABLE

LESSON 5: RANDOM VARIABLE TRANSFORMATION

LESSON 6: ONE DIMENSION RANDOM VARIABLE MOMENTUM

LESSON 7: TWO DIMENSIONS RANDOM VARIABLE MOMENTUM

LESSON 8: FUNCTIONS

LESSON 9: ONE DIMENSION DISCRETE RANDOM DISTRIBUTIONS

LESSON 10: ONE DIMENSION CONTINUOUS RANDOM DISTRIBUTIONS

LESSON 11: LIMIT THEORIES

LESSON 12: INTRODUCING SAMPLE THEORY

LESSON 13: SAMPLING OF NORMAL POPULATIONS

LESSON 14: POINT ESTIMATE

LESSON 15: ESTIMATE THROUGH CONFIDENCE INTERVAL

LESSON 16: BASICS OF HYPOTHESIS CONTRAST

LESSON 17: DISTRIBUTION HYPOTHESIS

During the course, the following teaching modalities will be developed; master classes, class-room practices and seminars.

The conceptual or theoretical framework of the subject will be developed in the master class modality. In the class-room practices students will solve problems related to statistics. The seminars will be guided by the problem based learning methodology. Students will work in groups to suggest solutions to statistic related problems.



In the case that sanitary conditions are an obstacle for presential teachig or/and evaluation, a non presential modality will activate, in which the students will be informed promptly.

• Final Assessment System
• Tools and qualification percentages:
  • Written test to be taken (%): 85
  • Oral defense (%): 5
  • Realization of Practical Work (exercises, cases or problems) (%): 10

The final grade for this subject depends on the valuation obtained in the following fields with the following weights.

Final individual test: it´s a written exam that consists in 4 problems to solve.

Group Work: Involves the making and exposition of works carried out in seminars.

The result obtained in the final exam represents an 85% of the final grade. The group work carried out in seminars represents a 15% (10% for the quality of the work, 5% for oral exposition). To approve the subject the students must obtain a grade that is equal or above 5.



For those students who justify not taking part in the planned evaluation method, the final exam´s grade will be the grade for the subject. Since the final exam constitutes over 50% of the subject´s grade´s weight, the act of not doing the exam automatically means that the student renounces the call and it will count as NON ATTENDED



Students can only use the following materials while doing the final exam:



1- a pen

2- non programmable calculator

3- two Din A4 sheets with formulas and statisticals

4- statistical tables



Items 3 and 4 must be stapled together



If health conditions don´t allow presential teaching and evaluation activities, a non presential modality will actívate and students will be punctually informed

The criterion for evaluation is the same as in the ordinary call. Students who didn´t pass the subject will maintain the grades referring group works.



For those students who justify not taking part in the planned evaluation method, the final exam´s grade will be the grade for the subject. Since the final exam constitutes over 50% of the subject´s grade´s weight, the act of not doing the exam automatically means that the student renounces the call and it will count as NON ATTENDED



Students can only use the following materials while doing the final exam:



1- a pen

2- non programmable calculator

3- two Din A4 sheets with formulas and statisticals

4- statistical tables



Items 3 and 4 must be stapled together



If health conditions don´t allow presential teaching and evaluation activities, a non presential modality will actívate and students will be punctually informed

MÉTODOS ESTADÍSTICOS EN INGENIERÍA. ETSI DE BILBAO

Basic bibliography

ARDANUY R., MARTÍN Q.

“ESTADÍSTICA PARA INGENIEROS”

HESPÉRIDES 1998



DEGROOT M.H.

"PROBABILIDAD Y ESTADISTICA"

ADDISON-WESLEY IBEROAMERICANA 2001



LOPEZ CACHERO M.

"FUNDAMENTOS Y METODOS DE ESTADISTICA"

PIRAMIDE 1990

In-depth bibliography

MONTGOMERY, D.C., RUNGER, G.C.
"PROBABILIDAD Y ESTADISTICA APLICADAS A LA INGENIERÍA”
MC GRAW-HILL 1996

DEVORE JL
“PROBABILIDAD Y ESTADÍSTICA PARA INGENIERÍA Y CIENCIAS “
THOMSON, MÉXICO. 2005

Journals

Estadística Española. INE.
Publicaciones del Instituto Vasco de Estadística /Euskal Estatistika-Erakundea.
Annals of Statistics. Institute of Mathematical Statistics (IMS)
ESAIM: Probability and Statistics. Société de Mathematiques Appliqués & Industrielles.
Societé de Mathematiques Appliqués & industrielles

Web addresses

www.ine.es
www.eustat.es
www.seeingstatistics.com
www.estadistico.com